# Attention! A Lightweight 2D Hand Pose Estimation Approach

arXiv: https://arxiv.org/abs/2001.08047 · HTML: https://mlsanity.com/paper/2001.08047/

## Key facts

- Cited by 9 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 22 Jan 2020.

## Built on by (most cited first, 9 of 9)

- [ZoomNAS: Searching for Whole-body Human Pose Estimation in the Wild](https://mlsanity.com/paper/2208.11547/) (2022)
- [Efficient Annotation and Learning for 3D Hand Pose Estimation: A Survey](https://mlsanity.com/paper/2206.02257/) (2022)
- [Domain Adaptive Hand Keypoint and Pixel Localization in the Wild](https://mlsanity.com/paper/2203.08344/) (2022)
- [In My Perspective, In My Hands: Accurate Egocentric 2D Hand Pose and Action Recognition](https://mlsanity.com/paper/2404.09308/) (2024)
- [Hand tracking for clinical applications: validation of the Google MediaPipe Hand (GMH) and the depth-enhanced GMH-D frameworks](https://mlsanity.com/paper/2308.01088/) (2023)
- [Temporally Guided Articulated Hand Pose Tracking in Surgical Videos](https://mlsanity.com/paper/2101.04281/) (2021)
- [Fast Monocular Hand Pose Estimation on Embedded Systems](https://mlsanity.com/paper/2102.07067/) (2021)
- [Efficient, Self-Supervised Human Pose Estimation with Inductive Prior Tuning](https://mlsanity.com/paper/2311.02815/) (2023)
- [A Review on Human Pose Estimation](https://mlsanity.com/paper/2110.06877/) (2021)

## Abstract (arXiv)

Vision based human pose estimation is an non-invasive technology for Human-Computer Interaction (HCI). Direct use of the hand as an input device provides an attractive interaction method, with no need for specialized sensing equipment, such as exoskeletons, gloves etc, but a camera. Traditionally, HCI is employed in various applications spreading in areas including manufacturing, surgery, entertainment industry and architecture, to mention a few. Deployment of vision based human pose estimation algorithms can give a breath of innovation to these applications. In this letter, we present a novel Convolutional Neural Network architecture, reinforced with a Self-Attention module that it can be deployed on an embedded system, due to its lightweight nature, with just 1.9 Million parameters. The source code and qualitative results are publicly available.